{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing GCN in PyTorch Geometric\n",
    "<hr/>\n",
    "\n",
    "![gcn_arch](img/gcn_architecture.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the Dataset\n",
    "\n",
    "Today we will be using the 'Cora' Dataset.The Cora dataset consists of Machine Learning papers. These papers are classified into one of the following seven classes:\n",
    "\t\tCase_Based\n",
    "\t\tGenetic_Algorithms\n",
    "\t\tNeural_Networks\n",
    "\t\tProbabilistic_Methods\n",
    "\t\tReinforcement_Learning\n",
    "\t\tRule_Learning\n",
    "\t\tTheory\n",
    "\n",
    "The papers were selected in a way such that in the final corpus every paper cites or is cited by atleast one other paper. There are 2708 papers in the whole corpus. \n",
    "\n",
    "__We will be using it for predicting the class to which a given paper belongs__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working\n",
    "\n",
    "Most  of the working is same as for the PyTorch version, except that we will be using a adjacency list representation instead of adjacency matrix. Advantage of using adjacency list is that that all the edges can be stores in $O(|E|)$ where $|E|$ is the  number of edges, wheres in adjacency matrix it is $O(|V|^2)$ where $|V|$ is number of vertices. So if number of edges is much less than number of vertices squared then adjacency list can save a lot of memory and time.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if we switch from adjacency matrix to adjacency list then the formulas involving the Adjacency Matrix will also have to be changed. Luckily it turns out that the formulas can be changed pretty easily. \n",
    "![img](img/formula.png)\n",
    "\n",
    "__Here $aggregate(A,X)(i)$ gives the i'th row of the target__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eu0admk-ngEI"
   },
   "outputs": [],
   "source": [
    "from torch_geometric.datasets import Planetoid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "jTFdN26FQPe9",
    "outputId": "2ba3b724-115b-4421-e811-d9332313bd9d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "dataset = Planetoid(root='/tmp/Cora', name='Cora')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Graph Convolution Layer\n",
    "\n",
    "The graph convolution class handles the task of taking the adjacency list and feature matrix and outputing the required input for next layer, which can be another graph convolutional layer or maybe some other class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z2IWFO0ARpim"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import MessagePassing\n",
    "from torch_geometric.utils import add_self_loops, degree\n",
    "\n",
    "class GraphConvolution(MessagePassing):\n",
    "    def __init__(self, in_channels, out_channels,bias=True, **kwargs):\n",
    "        super(GraphConvolution, self).__init__(aggr='add', **kwargs)\n",
    "        self.lin = torch.nn.Linear(in_channels, out_channels,bias=bias)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))\n",
    "        x = self.lin(x)\n",
    "        return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x)\n",
    "\n",
    "    def message(self, x_j, edge_index, size):\n",
    "        row, col = edge_index\n",
    "        deg = degree(row, size[0], dtype=x_j.dtype)\n",
    "        deg_inv_sqrt = deg.pow(-0.5)\n",
    "        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]\n",
    "        return norm.view(-1, 1) * x_j\n",
    "\n",
    "    def update(self, aggr_out):\n",
    "        return aggr_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'Net' class is the one where all the layers are combined together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EobH120SQUbi"
   },
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self,nfeat, nhid, nclass, dropout):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = GraphConvolution(nfeat, nhid)\n",
    "        self.conv2 = GraphConvolution(nhid, nclass)\n",
    "        self.dropout=dropout\n",
    "\n",
    "    def forward(self, data):\n",
    "        x, edge_index = data.x, data.edge_index\n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = F.relu(x)\n",
    "        x = F.dropout(x, self.dropout, training=self.training)\n",
    "        x = self.conv2(x, edge_index)\n",
    "\n",
    "        return F.log_softmax(x, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jXapRZk4bzIu"
   },
   "outputs": [],
   "source": [
    "nfeat=dataset.num_node_features\n",
    "nhid=16\n",
    "nclass=dataset.num_classes\n",
    "dropout=0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RSygvw7mQZuj"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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     ]
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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     ]
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
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      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = Net(nfeat, nhid, nclass, dropout).to(device)\n",
    "data = dataset[0].to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "\n",
    "model.train()\n",
    "for epoch in range(200):\n",
    "    optimizer.zero_grad()\n",
    "    out = model(data)\n",
    "    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "zBT-dFKkQdGp",
    "outputId": "9cfc622c-e4b5-4248-d0f9-4763df3b12b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8050\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n",
      "/opt/conda/conda-bld/pytorch_1565272271120/work/aten/src/ATen/native/IndexingUtils.h:20: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "_, pred = model(data).max(dim=1)\n",
    "correct = float (pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())\n",
    "acc = correct / data.test_mask.sum().item()\n",
    "print('Accuracy: {:.4f}'.format(acc))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Untitled4.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
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